Distribution and Precipitation Mechanism of TiN in Industrial Pure Iron Bloom

Liu, Xin; Bao, Yanping; Gu, Chao; Wang, Min; Yang, Jing; Chen, Botao

doi:10.1002/srin.202200481

Cited by 2 publications

(1 citation statement)

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“…If an alloy that is expensive or highly polluting during the production process is used, the cost will increase and the environment will become polluted. [5][6][7] 2) During the tapping process, splashing molten steel and steel slag can easily damage the ladle car scale, causing significant errors ladle car weighing indicator, rendering it unusable for weighing. The tapping weight can only be obtained by using a crane scale after lifting the ladle.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

Xue,

Xu,

Bao

et al. 2024

steel research int.

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Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis–whale optimization algorithm–backpropagation algorithm (PCA–WOA–BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted; the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately.

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Section: Introductionmentioning

confidence: 99%